Abstract

Color code is widely employed in coded structured light to reconstruct the three-dimensional shape of objects. Before determining the correspondence, a very important step is to identify the color code. Until now, the lack of an effective evaluation standard has hindered the progress in this unsupervised classification. In this paper, we propose a framework based on the benchmark to explore the new frontier. Two basic facets of the color code identification are discussed, including color feature selection and clustering algorithm design. First, we adopt analysis methods to evaluate the performance of different color features, and the order of these color features in the discriminating power is concluded after a large number of experiments. Second, in order to overcome the drawback of K-means, a decision-directed method is introduced to find the initial centroids. Quantitative comparisons affirm that our method is robust with high accuracy, and it can find or closely approach the global peak.

© 2012 Optical Society of America

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  1. F. Chen, G. Brown, and M. Song, “Overview of three-dimensional shape measurement using optical methods,” Opt. Eng. 39, 10–22 (2000).
    [CrossRef]
  2. F. Blais, “Review of 20 years of range sensor development,” J. Electron. Imaging 13, 231–243 (2004).
    [CrossRef]
  3. I. Albitar, P. Graebling, and C. Doignon, “Robust structured light coding for 3d reconstruction,” in 11th International Conference on Computer Vision (IEEE, 2007), pp. 1–6.
  4. J. Salvi, J. Pages, and J. Batlle, “Pattern codification strategies in structured light systems,” Pattern Recogn. 37, 827–849 (2004).
    [CrossRef]
  5. J. Salvi, S. Fernandez, T. Pribanic, and X. Llado, “A state of the art in structured light patterns for surface profilometry,” Pattern Recogn. 43, 2666–2680 (2010).
    [CrossRef]
  6. L. Zhang, B. Curless, and S. Seitz, “Rapid shape acquisition using color structured light and multi-pass dynamic programming,” in The 1st IEEE International Symposium on 3D Data Processing, Visualization, and Transmission (IEEE, 2002), pp. 24–36.
  7. P. Fechteler, P. Eisert, and J. Rurainsky, “Fast and high resolution 3d face scanning,” in IEEE International Conference on Image Processing (IEEE, 2007), pp. 81–84.
  8. P. Fechteler and P. Eisert, “Adaptive color classification for structured light systems,” IET Computer Vision 3, 49–59 (2009).
  9. H. Hugli and G. Maitre, “Generation and use of color pseudorandom sequences for coding structured light in active ranging,” Proc. SPIE 1010, 75–84 (1989).
  10. S. Chen, Y. Li, and J. Zhang, “Vision processing for realtime 3-D data acquisition based on coded structured light,” IEEE Trans. Image Process. 17, 167–176 (2008).
    [CrossRef]
  11. J. Salvi, J. Batlle, and E. Mouaddib, “A robust-coded pattern projection for dynamic 3D scene measurement,” Pattern Recogn. Lett. 19, 1055–1065 (1998).
    [CrossRef]
  12. F. MacWilliams and N. Sloane, “Pseudo-random sequences and arrays,” Proc. IEEE 64, 1715–1729 (1976).
    [CrossRef]
  13. X. Zhang and L. Zhu, “Robust calibration of a color structured light system using color correction,” in Proceedings of the 2nd International Conference on Intelligent Robotics and Applications (Springer-Verlag, 2009), pp. 936–946.
  14. K. Boyer and A. Kak, “Color-encoded structured light for rapid active ranging,” IEEE Trans. Pattern Anal. Machine Intell. 9, 14–28 (1987).
    [CrossRef]
  15. R. Xu and D. Wunsch, “Survey of clustering algorithms,” IEEE Trans. Neural Netw. 16, 645–678 (2005).
    [CrossRef]
  16. D. Caspi, N. Kiryati, and J. Shamir, “Range imaging with adaptive color structured light,” IEEE Trans. Pattern Anal. Machine Intell. 20, 470–480 (1998).
    [CrossRef]
  17. C. Chen, Y. Hung, C. Chiang, and J. Wu, “Range data acquisition using color structured lighting and stereo vision,” Image Vis. Comput. 15, 445–456 (1997).
    [CrossRef]
  18. C. Je, S. W. Lee, and R.-H. Park, “High-contrast color-stripe pattern for rapid structured-light range imaging,” in 8th European Conference on Computer Vision (Springer, 2004), pp. 95–107.
  19. J. Pages, J. Salvi, C. Collewet, and J. Forest, “Optimised De Bruijn patterns for one-shot shape acquisition,” Image Vis. Comput. 23, 707–720 (2005).
    [CrossRef]
  20. R. Morano, C. Ozturk, R. Conn, S. Dubin, S. Zietz, and J. Nissanov, “Structured light using pseudorandom codes,” IEEE Trans. Pattern Anal. Machine Intell. 20, 322–327 (1998).
    [CrossRef]
  21. H. Li, R. Straub, and H. Prautzsch, “Structured light based reconstruction under local spatial coherence assumption,” in the Third International Symposium on 3D Data Processing, Visualization, and Transmission (IEEE, 2006), pp. 575–582.
  22. T. Koninckx and L. Van Gool, “Real-time range acquisition by adaptive structured light,” IEEE Trans. Pattern Anal. Machine Intell. 28, 432–445 (2006).
    [CrossRef]
  23. H. Kawasaki, R. Furukawa, R. Sagawa, and Y. Yagi, “Dynamic scene shape reconstruction using a single structured light pattern,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1–8.
  24. K. Sato and S. Inokuchi, “Three-dimensional surface measurement by space encoding range imaging,” J. Robot. Syst. 2, 27–39 (1985).
  25. S. Rusinkiewicz, O. Hall-Holt, and M. Levoy, “Real-time 3D model acquisition,” in ACM Transactions on Graphics (ACM, 2002), pp. 438–446.
  26. X. Zhang and L. Zhu, “Determination of edge correspondence using color codes for one-shot shape acquisition,” Opt. Lasers Eng. 49, 97–103 (2011).
    [CrossRef]
  27. R. Fisher and D. Naidu, “A comparison of algorithms for subpixel peak detection,” Image Technology, Advances in Image Processing, Multimedia and Machine Vision, (Springer Verlag, 1996), pp. 385–404.
  28. T. Koninckx, I. Geys, T. Jaeggli, L. Van Gool, and B. Leuven, “A graph cut based adaptive structured light approach for real-time range acquisition,” in International Symposium on 3D Data Processing, Visualization and Transmission (IEEE, 2004), pp. 413–421.
  29. X. Zhang, L. Zhu, and Y. Li, “Indirect decoding edges for one-shot shape acquisition,” J. Opt. Soc. Am. A 28, 651–661 (2011).
    [CrossRef]
  30. X. Zhang, L. Zhu, and L. Chu, “Evaluation of coded structured light methods using ground truth,” in IEEE 5th International Conference on Cybernetics and Intelligent Systems (IEEE, 2011), pp. 117–123.
  31. T. Gevers and A. Smeulders, “Color based object recognition,” Pattern Recogn. 32, 453–464 (1999).
    [CrossRef]
  32. H. Cheng, X. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recogn. 34, 2259–2281 (2001).
    [CrossRef]
  33. J. Geusebroek, R. van den Boomgaard, A. Smeulders, and H. Geerts, “Color invariance,” IEEE Trans. Pattern Anal. Machine Intell. 23, 1338–1350 (2001).
    [CrossRef]
  34. T. Gevers and H. Stokman, “Classifying color transitions into shadow-geometry, illumination, highlight or material edges,” in International Conference on Image Processing (IEEE, 2000), pp. 521–524.
  35. E. Forgy, “Cluster analysis of multivariate data: efficiency versus interpretability of classifications,” Biometrics 21, 768–780 (1965).
  36. J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (1967), pp. 281–297.
  37. K. Sung and T. Poggio, “Example-based learning for view-based human face detection,” IEEE Trans. Pattern Anal. Machine Intell. 20, 39–51 (2002).
    [CrossRef]
  38. B. Curless and M. Levoy, “Better optical triangulation through spacetime analysis,” in The Fifth International Conference on Computer Vision (IEEE, 1995), pp. 987–994.

2011

X. Zhang and L. Zhu, “Determination of edge correspondence using color codes for one-shot shape acquisition,” Opt. Lasers Eng. 49, 97–103 (2011).
[CrossRef]

X. Zhang, L. Zhu, and Y. Li, “Indirect decoding edges for one-shot shape acquisition,” J. Opt. Soc. Am. A 28, 651–661 (2011).
[CrossRef]

2010

J. Salvi, S. Fernandez, T. Pribanic, and X. Llado, “A state of the art in structured light patterns for surface profilometry,” Pattern Recogn. 43, 2666–2680 (2010).
[CrossRef]

2009

P. Fechteler and P. Eisert, “Adaptive color classification for structured light systems,” IET Computer Vision 3, 49–59 (2009).

2008

S. Chen, Y. Li, and J. Zhang, “Vision processing for realtime 3-D data acquisition based on coded structured light,” IEEE Trans. Image Process. 17, 167–176 (2008).
[CrossRef]

2006

T. Koninckx and L. Van Gool, “Real-time range acquisition by adaptive structured light,” IEEE Trans. Pattern Anal. Machine Intell. 28, 432–445 (2006).
[CrossRef]

2005

R. Xu and D. Wunsch, “Survey of clustering algorithms,” IEEE Trans. Neural Netw. 16, 645–678 (2005).
[CrossRef]

J. Pages, J. Salvi, C. Collewet, and J. Forest, “Optimised De Bruijn patterns for one-shot shape acquisition,” Image Vis. Comput. 23, 707–720 (2005).
[CrossRef]

2004

F. Blais, “Review of 20 years of range sensor development,” J. Electron. Imaging 13, 231–243 (2004).
[CrossRef]

J. Salvi, J. Pages, and J. Batlle, “Pattern codification strategies in structured light systems,” Pattern Recogn. 37, 827–849 (2004).
[CrossRef]

2002

K. Sung and T. Poggio, “Example-based learning for view-based human face detection,” IEEE Trans. Pattern Anal. Machine Intell. 20, 39–51 (2002).
[CrossRef]

2001

H. Cheng, X. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recogn. 34, 2259–2281 (2001).
[CrossRef]

J. Geusebroek, R. van den Boomgaard, A. Smeulders, and H. Geerts, “Color invariance,” IEEE Trans. Pattern Anal. Machine Intell. 23, 1338–1350 (2001).
[CrossRef]

2000

F. Chen, G. Brown, and M. Song, “Overview of three-dimensional shape measurement using optical methods,” Opt. Eng. 39, 10–22 (2000).
[CrossRef]

1999

T. Gevers and A. Smeulders, “Color based object recognition,” Pattern Recogn. 32, 453–464 (1999).
[CrossRef]

1998

R. Morano, C. Ozturk, R. Conn, S. Dubin, S. Zietz, and J. Nissanov, “Structured light using pseudorandom codes,” IEEE Trans. Pattern Anal. Machine Intell. 20, 322–327 (1998).
[CrossRef]

D. Caspi, N. Kiryati, and J. Shamir, “Range imaging with adaptive color structured light,” IEEE Trans. Pattern Anal. Machine Intell. 20, 470–480 (1998).
[CrossRef]

J. Salvi, J. Batlle, and E. Mouaddib, “A robust-coded pattern projection for dynamic 3D scene measurement,” Pattern Recogn. Lett. 19, 1055–1065 (1998).
[CrossRef]

1997

C. Chen, Y. Hung, C. Chiang, and J. Wu, “Range data acquisition using color structured lighting and stereo vision,” Image Vis. Comput. 15, 445–456 (1997).
[CrossRef]

1989

H. Hugli and G. Maitre, “Generation and use of color pseudorandom sequences for coding structured light in active ranging,” Proc. SPIE 1010, 75–84 (1989).

1987

K. Boyer and A. Kak, “Color-encoded structured light for rapid active ranging,” IEEE Trans. Pattern Anal. Machine Intell. 9, 14–28 (1987).
[CrossRef]

1985

K. Sato and S. Inokuchi, “Three-dimensional surface measurement by space encoding range imaging,” J. Robot. Syst. 2, 27–39 (1985).

1976

F. MacWilliams and N. Sloane, “Pseudo-random sequences and arrays,” Proc. IEEE 64, 1715–1729 (1976).
[CrossRef]

1965

E. Forgy, “Cluster analysis of multivariate data: efficiency versus interpretability of classifications,” Biometrics 21, 768–780 (1965).

Albitar, I.

I. Albitar, P. Graebling, and C. Doignon, “Robust structured light coding for 3d reconstruction,” in 11th International Conference on Computer Vision (IEEE, 2007), pp. 1–6.

Batlle, J.

J. Salvi, J. Pages, and J. Batlle, “Pattern codification strategies in structured light systems,” Pattern Recogn. 37, 827–849 (2004).
[CrossRef]

J. Salvi, J. Batlle, and E. Mouaddib, “A robust-coded pattern projection for dynamic 3D scene measurement,” Pattern Recogn. Lett. 19, 1055–1065 (1998).
[CrossRef]

Blais, F.

F. Blais, “Review of 20 years of range sensor development,” J. Electron. Imaging 13, 231–243 (2004).
[CrossRef]

Boyer, K.

K. Boyer and A. Kak, “Color-encoded structured light for rapid active ranging,” IEEE Trans. Pattern Anal. Machine Intell. 9, 14–28 (1987).
[CrossRef]

Brown, G.

F. Chen, G. Brown, and M. Song, “Overview of three-dimensional shape measurement using optical methods,” Opt. Eng. 39, 10–22 (2000).
[CrossRef]

Caspi, D.

D. Caspi, N. Kiryati, and J. Shamir, “Range imaging with adaptive color structured light,” IEEE Trans. Pattern Anal. Machine Intell. 20, 470–480 (1998).
[CrossRef]

Chen, C.

C. Chen, Y. Hung, C. Chiang, and J. Wu, “Range data acquisition using color structured lighting and stereo vision,” Image Vis. Comput. 15, 445–456 (1997).
[CrossRef]

Chen, F.

F. Chen, G. Brown, and M. Song, “Overview of three-dimensional shape measurement using optical methods,” Opt. Eng. 39, 10–22 (2000).
[CrossRef]

Chen, S.

S. Chen, Y. Li, and J. Zhang, “Vision processing for realtime 3-D data acquisition based on coded structured light,” IEEE Trans. Image Process. 17, 167–176 (2008).
[CrossRef]

Cheng, H.

H. Cheng, X. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recogn. 34, 2259–2281 (2001).
[CrossRef]

Chiang, C.

C. Chen, Y. Hung, C. Chiang, and J. Wu, “Range data acquisition using color structured lighting and stereo vision,” Image Vis. Comput. 15, 445–456 (1997).
[CrossRef]

Chu, L.

X. Zhang, L. Zhu, and L. Chu, “Evaluation of coded structured light methods using ground truth,” in IEEE 5th International Conference on Cybernetics and Intelligent Systems (IEEE, 2011), pp. 117–123.

Collewet, C.

J. Pages, J. Salvi, C. Collewet, and J. Forest, “Optimised De Bruijn patterns for one-shot shape acquisition,” Image Vis. Comput. 23, 707–720 (2005).
[CrossRef]

Conn, R.

R. Morano, C. Ozturk, R. Conn, S. Dubin, S. Zietz, and J. Nissanov, “Structured light using pseudorandom codes,” IEEE Trans. Pattern Anal. Machine Intell. 20, 322–327 (1998).
[CrossRef]

Curless, B.

B. Curless and M. Levoy, “Better optical triangulation through spacetime analysis,” in The Fifth International Conference on Computer Vision (IEEE, 1995), pp. 987–994.

L. Zhang, B. Curless, and S. Seitz, “Rapid shape acquisition using color structured light and multi-pass dynamic programming,” in The 1st IEEE International Symposium on 3D Data Processing, Visualization, and Transmission (IEEE, 2002), pp. 24–36.

Doignon, C.

I. Albitar, P. Graebling, and C. Doignon, “Robust structured light coding for 3d reconstruction,” in 11th International Conference on Computer Vision (IEEE, 2007), pp. 1–6.

Dubin, S.

R. Morano, C. Ozturk, R. Conn, S. Dubin, S. Zietz, and J. Nissanov, “Structured light using pseudorandom codes,” IEEE Trans. Pattern Anal. Machine Intell. 20, 322–327 (1998).
[CrossRef]

Eisert, P.

P. Fechteler and P. Eisert, “Adaptive color classification for structured light systems,” IET Computer Vision 3, 49–59 (2009).

P. Fechteler, P. Eisert, and J. Rurainsky, “Fast and high resolution 3d face scanning,” in IEEE International Conference on Image Processing (IEEE, 2007), pp. 81–84.

Fechteler, P.

P. Fechteler and P. Eisert, “Adaptive color classification for structured light systems,” IET Computer Vision 3, 49–59 (2009).

P. Fechteler, P. Eisert, and J. Rurainsky, “Fast and high resolution 3d face scanning,” in IEEE International Conference on Image Processing (IEEE, 2007), pp. 81–84.

Fernandez, S.

J. Salvi, S. Fernandez, T. Pribanic, and X. Llado, “A state of the art in structured light patterns for surface profilometry,” Pattern Recogn. 43, 2666–2680 (2010).
[CrossRef]

Fisher, R.

R. Fisher and D. Naidu, “A comparison of algorithms for subpixel peak detection,” Image Technology, Advances in Image Processing, Multimedia and Machine Vision, (Springer Verlag, 1996), pp. 385–404.

Forest, J.

J. Pages, J. Salvi, C. Collewet, and J. Forest, “Optimised De Bruijn patterns for one-shot shape acquisition,” Image Vis. Comput. 23, 707–720 (2005).
[CrossRef]

Forgy, E.

E. Forgy, “Cluster analysis of multivariate data: efficiency versus interpretability of classifications,” Biometrics 21, 768–780 (1965).

Furukawa, R.

H. Kawasaki, R. Furukawa, R. Sagawa, and Y. Yagi, “Dynamic scene shape reconstruction using a single structured light pattern,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1–8.

Geerts, H.

J. Geusebroek, R. van den Boomgaard, A. Smeulders, and H. Geerts, “Color invariance,” IEEE Trans. Pattern Anal. Machine Intell. 23, 1338–1350 (2001).
[CrossRef]

Geusebroek, J.

J. Geusebroek, R. van den Boomgaard, A. Smeulders, and H. Geerts, “Color invariance,” IEEE Trans. Pattern Anal. Machine Intell. 23, 1338–1350 (2001).
[CrossRef]

Gevers, T.

T. Gevers and A. Smeulders, “Color based object recognition,” Pattern Recogn. 32, 453–464 (1999).
[CrossRef]

T. Gevers and H. Stokman, “Classifying color transitions into shadow-geometry, illumination, highlight or material edges,” in International Conference on Image Processing (IEEE, 2000), pp. 521–524.

Geys, I.

T. Koninckx, I. Geys, T. Jaeggli, L. Van Gool, and B. Leuven, “A graph cut based adaptive structured light approach for real-time range acquisition,” in International Symposium on 3D Data Processing, Visualization and Transmission (IEEE, 2004), pp. 413–421.

Graebling, P.

I. Albitar, P. Graebling, and C. Doignon, “Robust structured light coding for 3d reconstruction,” in 11th International Conference on Computer Vision (IEEE, 2007), pp. 1–6.

Hall-Holt, O.

S. Rusinkiewicz, O. Hall-Holt, and M. Levoy, “Real-time 3D model acquisition,” in ACM Transactions on Graphics (ACM, 2002), pp. 438–446.

Hugli, H.

H. Hugli and G. Maitre, “Generation and use of color pseudorandom sequences for coding structured light in active ranging,” Proc. SPIE 1010, 75–84 (1989).

Hung, Y.

C. Chen, Y. Hung, C. Chiang, and J. Wu, “Range data acquisition using color structured lighting and stereo vision,” Image Vis. Comput. 15, 445–456 (1997).
[CrossRef]

Inokuchi, S.

K. Sato and S. Inokuchi, “Three-dimensional surface measurement by space encoding range imaging,” J. Robot. Syst. 2, 27–39 (1985).

Jaeggli, T.

T. Koninckx, I. Geys, T. Jaeggli, L. Van Gool, and B. Leuven, “A graph cut based adaptive structured light approach for real-time range acquisition,” in International Symposium on 3D Data Processing, Visualization and Transmission (IEEE, 2004), pp. 413–421.

Je, C.

C. Je, S. W. Lee, and R.-H. Park, “High-contrast color-stripe pattern for rapid structured-light range imaging,” in 8th European Conference on Computer Vision (Springer, 2004), pp. 95–107.

Jiang, X.

H. Cheng, X. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recogn. 34, 2259–2281 (2001).
[CrossRef]

Kak, A.

K. Boyer and A. Kak, “Color-encoded structured light for rapid active ranging,” IEEE Trans. Pattern Anal. Machine Intell. 9, 14–28 (1987).
[CrossRef]

Kawasaki, H.

H. Kawasaki, R. Furukawa, R. Sagawa, and Y. Yagi, “Dynamic scene shape reconstruction using a single structured light pattern,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1–8.

Kiryati, N.

D. Caspi, N. Kiryati, and J. Shamir, “Range imaging with adaptive color structured light,” IEEE Trans. Pattern Anal. Machine Intell. 20, 470–480 (1998).
[CrossRef]

Koninckx, T.

T. Koninckx and L. Van Gool, “Real-time range acquisition by adaptive structured light,” IEEE Trans. Pattern Anal. Machine Intell. 28, 432–445 (2006).
[CrossRef]

T. Koninckx, I. Geys, T. Jaeggli, L. Van Gool, and B. Leuven, “A graph cut based adaptive structured light approach for real-time range acquisition,” in International Symposium on 3D Data Processing, Visualization and Transmission (IEEE, 2004), pp. 413–421.

Lee, S. W.

C. Je, S. W. Lee, and R.-H. Park, “High-contrast color-stripe pattern for rapid structured-light range imaging,” in 8th European Conference on Computer Vision (Springer, 2004), pp. 95–107.

Leuven, B.

T. Koninckx, I. Geys, T. Jaeggli, L. Van Gool, and B. Leuven, “A graph cut based adaptive structured light approach for real-time range acquisition,” in International Symposium on 3D Data Processing, Visualization and Transmission (IEEE, 2004), pp. 413–421.

Levoy, M.

B. Curless and M. Levoy, “Better optical triangulation through spacetime analysis,” in The Fifth International Conference on Computer Vision (IEEE, 1995), pp. 987–994.

S. Rusinkiewicz, O. Hall-Holt, and M. Levoy, “Real-time 3D model acquisition,” in ACM Transactions on Graphics (ACM, 2002), pp. 438–446.

Li, H.

H. Li, R. Straub, and H. Prautzsch, “Structured light based reconstruction under local spatial coherence assumption,” in the Third International Symposium on 3D Data Processing, Visualization, and Transmission (IEEE, 2006), pp. 575–582.

Li, Y.

X. Zhang, L. Zhu, and Y. Li, “Indirect decoding edges for one-shot shape acquisition,” J. Opt. Soc. Am. A 28, 651–661 (2011).
[CrossRef]

S. Chen, Y. Li, and J. Zhang, “Vision processing for realtime 3-D data acquisition based on coded structured light,” IEEE Trans. Image Process. 17, 167–176 (2008).
[CrossRef]

Llado, X.

J. Salvi, S. Fernandez, T. Pribanic, and X. Llado, “A state of the art in structured light patterns for surface profilometry,” Pattern Recogn. 43, 2666–2680 (2010).
[CrossRef]

MacQueen, J.

J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (1967), pp. 281–297.

MacWilliams, F.

F. MacWilliams and N. Sloane, “Pseudo-random sequences and arrays,” Proc. IEEE 64, 1715–1729 (1976).
[CrossRef]

Maitre, G.

H. Hugli and G. Maitre, “Generation and use of color pseudorandom sequences for coding structured light in active ranging,” Proc. SPIE 1010, 75–84 (1989).

Morano, R.

R. Morano, C. Ozturk, R. Conn, S. Dubin, S. Zietz, and J. Nissanov, “Structured light using pseudorandom codes,” IEEE Trans. Pattern Anal. Machine Intell. 20, 322–327 (1998).
[CrossRef]

Mouaddib, E.

J. Salvi, J. Batlle, and E. Mouaddib, “A robust-coded pattern projection for dynamic 3D scene measurement,” Pattern Recogn. Lett. 19, 1055–1065 (1998).
[CrossRef]

Naidu, D.

R. Fisher and D. Naidu, “A comparison of algorithms for subpixel peak detection,” Image Technology, Advances in Image Processing, Multimedia and Machine Vision, (Springer Verlag, 1996), pp. 385–404.

Nissanov, J.

R. Morano, C. Ozturk, R. Conn, S. Dubin, S. Zietz, and J. Nissanov, “Structured light using pseudorandom codes,” IEEE Trans. Pattern Anal. Machine Intell. 20, 322–327 (1998).
[CrossRef]

Ozturk, C.

R. Morano, C. Ozturk, R. Conn, S. Dubin, S. Zietz, and J. Nissanov, “Structured light using pseudorandom codes,” IEEE Trans. Pattern Anal. Machine Intell. 20, 322–327 (1998).
[CrossRef]

Pages, J.

J. Pages, J. Salvi, C. Collewet, and J. Forest, “Optimised De Bruijn patterns for one-shot shape acquisition,” Image Vis. Comput. 23, 707–720 (2005).
[CrossRef]

J. Salvi, J. Pages, and J. Batlle, “Pattern codification strategies in structured light systems,” Pattern Recogn. 37, 827–849 (2004).
[CrossRef]

Park, R.-H.

C. Je, S. W. Lee, and R.-H. Park, “High-contrast color-stripe pattern for rapid structured-light range imaging,” in 8th European Conference on Computer Vision (Springer, 2004), pp. 95–107.

Poggio, T.

K. Sung and T. Poggio, “Example-based learning for view-based human face detection,” IEEE Trans. Pattern Anal. Machine Intell. 20, 39–51 (2002).
[CrossRef]

Prautzsch, H.

H. Li, R. Straub, and H. Prautzsch, “Structured light based reconstruction under local spatial coherence assumption,” in the Third International Symposium on 3D Data Processing, Visualization, and Transmission (IEEE, 2006), pp. 575–582.

Pribanic, T.

J. Salvi, S. Fernandez, T. Pribanic, and X. Llado, “A state of the art in structured light patterns for surface profilometry,” Pattern Recogn. 43, 2666–2680 (2010).
[CrossRef]

Rurainsky, J.

P. Fechteler, P. Eisert, and J. Rurainsky, “Fast and high resolution 3d face scanning,” in IEEE International Conference on Image Processing (IEEE, 2007), pp. 81–84.

Rusinkiewicz, S.

S. Rusinkiewicz, O. Hall-Holt, and M. Levoy, “Real-time 3D model acquisition,” in ACM Transactions on Graphics (ACM, 2002), pp. 438–446.

Sagawa, R.

H. Kawasaki, R. Furukawa, R. Sagawa, and Y. Yagi, “Dynamic scene shape reconstruction using a single structured light pattern,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1–8.

Salvi, J.

J. Salvi, S. Fernandez, T. Pribanic, and X. Llado, “A state of the art in structured light patterns for surface profilometry,” Pattern Recogn. 43, 2666–2680 (2010).
[CrossRef]

J. Pages, J. Salvi, C. Collewet, and J. Forest, “Optimised De Bruijn patterns for one-shot shape acquisition,” Image Vis. Comput. 23, 707–720 (2005).
[CrossRef]

J. Salvi, J. Pages, and J. Batlle, “Pattern codification strategies in structured light systems,” Pattern Recogn. 37, 827–849 (2004).
[CrossRef]

J. Salvi, J. Batlle, and E. Mouaddib, “A robust-coded pattern projection for dynamic 3D scene measurement,” Pattern Recogn. Lett. 19, 1055–1065 (1998).
[CrossRef]

Sato, K.

K. Sato and S. Inokuchi, “Three-dimensional surface measurement by space encoding range imaging,” J. Robot. Syst. 2, 27–39 (1985).

Seitz, S.

L. Zhang, B. Curless, and S. Seitz, “Rapid shape acquisition using color structured light and multi-pass dynamic programming,” in The 1st IEEE International Symposium on 3D Data Processing, Visualization, and Transmission (IEEE, 2002), pp. 24–36.

Shamir, J.

D. Caspi, N. Kiryati, and J. Shamir, “Range imaging with adaptive color structured light,” IEEE Trans. Pattern Anal. Machine Intell. 20, 470–480 (1998).
[CrossRef]

Sloane, N.

F. MacWilliams and N. Sloane, “Pseudo-random sequences and arrays,” Proc. IEEE 64, 1715–1729 (1976).
[CrossRef]

Smeulders, A.

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Figures (13)

Fig. 1.
Fig. 1.

Some examples of color pattern. (a) stripe pattern [6], (b) multislit pattern [7,8], (c) multislit based on pseudorandom sequence [9], (d) a tessellated pattern [10], (e) grid pattern [11], (f) M-matrix [12,13].

Fig. 2.
Fig. 2.

Related factors about the formation of the captured image.

Fig. 3.
Fig. 3.

Pipeline of coded structured light.

Fig. 4.
Fig. 4.

Portable structured light system and seven subjects: (a) the portable structured light system, (b) girl, (c) Marseilles, (d) hands, (e) box and doll, (f) feet and pig, (g) Cutter, and (h) frog.

Fig. 5.
Fig. 5.

Ground truth of correspondences: (a) girl, (b) Marseilles, (c) hands, (d) box and doll, (e) frog, (f) feet and pig, and (g) cutter.

Fig. 6.
Fig. 6.

Captured images (O) and theoretical images (T). (a) Girl, (b) Marseilles, (c) hands, (d) box and doll, (e) feet and pig, (f) cutter, and (g) frog.

Fig. 7.
Fig. 7.

Labeled color features of box and doll: (a) RGB, (b) RGB with color correction, (c) l1l2l3, (d) c1c2c3, (e) Nrgb, (f) Rrgb, (g) CIE lab, (h) HSI, (i) E, (j) H*S*, and (k) C.

Fig. 8.
Fig. 8.

Class separability measure J1 of different color features on different objects. O, the original color feature; P, the projection of the original feature on the plane: (a) girl, (b) Marseilles, (c) hands, (d) box and doll, (e) feet and pig, (f) cutter, and (g) frog.

Fig. 9.
Fig. 9.

Color features: (a) lil2l3, (b) c1c2c3, (c) Nrgb, and (d) Rrgb.

Fig. 10.
Fig. 10.

Accuracies of different clustering algorithms on different objects: (a) girl, (b) Marseilles, (c) hands, (d) box and doll, (e) feet and pig, (f) cutter, (g) frog, (h) the appearance times of clustering algorithms for different color features.

Fig. 11.
Fig. 11.

Diagram of the projected color values (circles), theoretical centroids (squares), and convergence points (dots). (a) Rrgb, (b) c1c2c2, (c) Nrgb, and (d) HS.

Fig. 12.
Fig. 12.

Noise point (red) and correct surface (cyan) for different objects, such as Marseilles (a), box (b), and frog (c), using RGB and Rrgb, respectively.

Fig. 13.
Fig. 13.

Surfaces of multiple facial expressions.

Tables (7)

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Table 1. Various Color Features

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Table 2. List of Clustering Algorithms

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Table 3. Projected Color in Different Color Space

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Table 4. Results of Three Kinds of K-means Using Rrgba

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Table 5. Results of Three Kinds of K-means Using c1c2c3a

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Table 6. Results of Three Kinds of K-means Using Nrgba

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Table 7. Results of Three Kinds of K-means Using HSa

Equations (3)

Equations on this page are rendered with MathJax. Learn more.

[XYZ]=[0.6070.1740.2000.2990.5870.1140.0000.0661.116][RGB]
[E^E^λE^λλ]=[0.060.630.270.30.040.350.340.60.17][RGB]
J1=trace{SB}trace{Sw},

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